The present disclosure relates in general to the computer-based management of retail operations. More specifically, the present disclosure relates to systems and methodologies for providing distributed computations that support dynamic inventory valuation in real-time omnichannel retail operations.
The internet and a variety of wired and wireless mobile technologies have significantly impacted the ways in which retailers interface with their actual and potential customers. For example, the phrase “brick-and-mortar” is used to describe a business that is located in a building as opposed to an online shopping destination, door-to-door sales, a kiosk or other similar sites not housed within a structure. Brick-and-mortar operations traditionally dealt with their customers face to face in an office or store. As a response to their online-only competitors, many brick-and-mortar retailers began to offer merchandise for sale through the retailer's website, thus offering customers the option of purchasing through the same type of online mail-order channel provided by online-only retailers. As a result, major retailers now engage their customers and execute transactions across multiple channels.
From the supply-chain perspective, these retailers have, in addition to adding e-commerce fulfilment centers, started to employ the logistical concept of “ship-from-store” (SFS) that uses inventory located at a brick-and-mortar store to fulfil e-commerce orders for a product originating at any location. Consequently, the store inventory is scarce resource shared by several sales channels. This poses at least two new challenges for such retailers. First, their revenue management system must take into account this SFS fulfilment logic. Second, and more important, these retailers must decide in real-time the most profitable store location from which to deplete inventory in order to meet an incoming e-commerce order.
The phrase “omnichannel retailing” or “multichannel retailing” is used to describe retail operations that offer merchandise for sale through a variety of channels in a customer's shopping experience, including conducting research before a purchase, visiting brick-and-mortar stores, browsing online stores, browsing mobile application (i.e., “app”) stores, making telephone purchases and any other method of transacting with a retailer. Providing customers with the ability to purchase merchandise across multiple channels presents a new and significant management challenge for retailers. A variety of computer-based systems have been developed to support the performance of traditional retail functions in an omnichannel environment. For example, there now exist omnichannel revenue management systems (OC-RMSs) that analyze different variables (e.g., forecasted demand) over different channels covering a future time period to optimize prices and inventory over that time period. The OC-RMS calculated purchase price of a product, and thus the valuation of inventory, is not a fixed value but can vary considerably over time, as well as the fulfillment locations and the sales channels. There also exist omnichannel real time applications (OC-RTAs) (e.g., warehouse and order management/fulfillment systems) that manage time-sensitive, short term retail operations such as order fulfillment, product recommendations, etc. in an omnichannel environment.
A typical OC-RTA, such an e-commerce fulfilment application, fills a given order from a location that is chosen to minimize cost-to-serve and lost revenue OC-RTAs track costs accurately but are not fully revenue aware, which leads to margin leak and lost brick sales. It would be beneficial for retailers to have the capability to generate their OC-RMS inventory valuation data in real time in order to effectively and efficiently integrate such real time omnichannel inventory valuation data into OC-RTAs. However, the large scale nonlinear, nonconvex planning models of a typical OC-RMS make the extraction of real time, demand driven inventory valuation data from an OC-RMS complicated, resource intensive and time consuming.
There is no known system that efficiently and cost effectively extracts real time inventory valuation data from an OC-RMS, nor is there a known system that efficiently and effectively integrates such real time inventory valuations into the dynamic and real-time operating environment of an OC-RTA.